Bibliographic record
Abstract
A broad spectrum of space application domains are increasingly making use of heterogeneous and large volumes of data with varying degrees of human in the loop. Applications include critical areas such as space habitat management, healthcare delivery, and emergency management while the International Space Station Environmental Control and Life Support System (ECLSS) as well as PHM for Human Health and Performance (PHM4HHP) serve as examples of the applications. This paper suggests a discussion on implementation of the Model Driven Engineering paradigm (a.k.a. Model Based Systems Engineering approach) for PHM focusing on HHP on crewed space exploration missions and introduces a conceptual model and framework - the PHM4HHP Domain Specific Language - to support a data-centric and model-driven approach and develop requirements for integration of heterogeneous models and their respective data for the entire life-cycle of the Health Support System (HeSS) being designed to validate on the International Space Station (ISS). While the discussion starts with focusing on PHM4HHP concepts that make the PHM4HHP domain different from conventional healthcare delivery and is to elaborate the PHM foundations for HHP in terms of basic concepts, driving principles, and current practices to employ PHM4HHP on space exploration missions, the second part is an effort to convert the PHM language to a UML-based metamodel in terms of the promising model-driven and data-centric approach including inherent exercises such as profiling, mapping, and building metamodels. The paper also articulates key requirements in terms of predictive diagnostics providing early and actionable real-time warnings of impending health issues that otherwise would have gone undetected.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.034 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".